esttable {fixest} | R Documentation

## Facility to display the results of multiple `fixest` estimations.

### Description

This function aggregates the results of multiple estimations and display them in the form of only one table whose row names are the variables and the columns contain the coefficients and standard-errors.

### Usage

    esttable(..., se = c("standard", "white", "cluster", "twoway",
      "threeway", "fourway"), dof = TRUE, cluster, depvar, drop, order,
      digits = 4, pseudo = TRUE, convergence, signifCode = c(`***` =
      0.001, `**` = 0.01, `*` = 0.05, . = 0.1), titles, keepFactors = FALSE,
      family, bic = TRUE, loglik = FALSE)

### Arguments

`...` | Used to capture different `fixest` objects (obtained with `femlm`, `feols` or `feglm`). Note that any other type of element is discarded. Note that you can give a list of `fixest` objects.
`se` | Character scalar. Which kind of standard error should be computed: “standard”, “White”, “cluster”, “twoway”, “threeway” or “fourway”? By default if there are clusters in the estimation: `se = "cluster"`, otherwise `se = "standard"`. Note that this argument can be implicitly deduced from the argument `cluster`.
`dof` | Logical, default is `TRUE`. Should there be a degree of freedom correction to the standard errors of the coefficients?

`cluster`

Tells how to cluster the standard-errors (if clustering is requested). Can be either a list of vectors, a character vector of variable names, a formula or an integer vector. Assume we want to perform 2-way clustering over `var1` and `var2` contained in the data.frame `base` used for the estimation. All the following `cluster` arguments are valid and do the same thing: `cluster = base[, c("var1, "var2")]}, \code{cluster = c("var1, "var2")`, `cluster = ~var1+var2`. If the two variables were used as clusters in the estimation, you could further use `cluster = 1:2` or leave it blank with `se = "twoway"` (assuming `var1` [resp. `var2`] was the 1st [res. 2nd] cluster).

`depvar` | Logical, default is missing. Whether a first line containing the dependent variables should be shown. By default, the dependent variables are shown only if they differ across models.
`drop` | Character vector. This element is used if some variables are not to be displayed. This should be a regular expression (see `regex` help for more info). There can be more than one regular expression. Each variable satisfying the regular expression will be discarded.
`order` | Character vector. This element is used if the user wants the variables to be ordered in a certain way. This should be a regular expression (see `regex` help for more info). There can be more than one regular expression. The variables satisfying the first regular expression will be placed first, then the order follows the sequence of regular expressions.
`digits` | Integer, default is 4. The number of digits to be displayed.
`pseudo` | Logical, default is `TRUE`. Should the pseudo R2 be displayed?
`convergence` | Logical, default is missing. Should the convergence state of the algorithm be displayed? By default, convergence information is displayed if at least one model did not converge.
`signifCode` | Named numeric vector, used to provide the significance codes with respect to the p-value of the coefficients. Default is `c("***"=0.001, "**"=0.01, "*"=0.05, "."=0.10)`.
`titles` | A character vector. The length must match the number of models.
`keepFactors` | Logical, default is `TRUE`. If `FALSE`, then factor variables are displayed as fixed-effects and no coefficient is shown.
`family` | A logical, default is missing. Whether to display the families of the models. By default this line is displayed when at least two models are from different families.
`bic` | Logical, default is `TRUE`.Should the BIC be reported?
`loglik` | Logical, default is `TRUE`. Should the log-likelihood be reported?

### Value

Returns a data.frame containing the formatted results.

### Author(s)

Laurent Berge

### See Also

See also the main estimation functions `femlm`, `feols` or `feglm`. Use `summary.fixest` to see the results with the appropriate standard-errors, `fixef.fixest` to extract the cluster coefficients, and the functions `esttable` and `esttex` to visualize the results of multiple estimations.

### Examples

    # two fitted models with different expl. variables:
    res1 = femlm(Sepal.Length ~ Sepal.Width + Petal.Length +
                Petal.Width | Species, iris)
    # estimation without clusters
    res2 = update(res1, . ~ Sepal.Width | 0)

    # We export the two results in one Latex table:
    esttable(res1, res2)

    # With clustered standard-errors + showing the dependent variable
    esttable(res1, res2, se = "cluster", cluster = iris$Species, depvar = TRUE)

    # Changing the model names + the order of the variables
    # + dropping the intercept.
    esttable(model_1 = res1, res2,
              order = c("Width", "Petal"), drop = "Int",
              signifCode = c("**" = 0, "*" = 0.2, "n.s."=1))

[Package fixest version 0.1.0 Index]